Integrated approach on stored grain quality management with CO2 monitoring-A review

Ramachandran, Rani Puthukulangara

doi:10.1016/j.jspr.2022.101950

Cited by 25 publications

(11 citation statements)

References 93 publications

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“…The adoption of the Internet of Things (IoT) paradigm enables the communication of storage environments with the product through sensors and wireless connections (Chandra & Ravi, 2016; Gilmore et al, 2019; Lopes et al, 2008; Onibonoje et al, 2019; Wang et al, 2006; Zhang et al, 2021). This communication allows, among other facilities, the continuous recording of data on the state of such objects during their use or the state of the environment in which they are inserted (Akyildiz & Stuntebeck, 2006; Ramachandran, 2022; Yasuda et al, 2012). In conjunction with the monitoring system, the application of artificial neural networks (ANNs) can predict the grain quality under different storage conditions (Abraham et al, 2020; Azevedo et al, 2015; Balducci et al, 2018; Barbosa et al, 2011; Lutz & Coradi, 2021).…”

Section: Introductionmentioning

confidence: 99%

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

Lutz

Coradi

Jaques

et al. 2022

J Food Process Engineering

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The use of silo and raffia bags for the temporary grain storage has been increasing in recent years. However, the methods for monitoring a stored product are limited to visual inspections and sampling. Thus, this research aimed to real-time equilibrium moisture content monitoring to predict grain quality of corn stored in different conditions in silo and raffia bags using wireless sensor network prototype, Internet of Things (IoT) platform, and neural network algorithms. Experiments were conducted using corn grain with two initial water contents of 13% and 18% (w.b.), three storage environments with temperatures of 30, 23, and 17 C, and two types of packaging, that is, silo and raffia bags, for a 3-month storage evaluation. During the monitoring

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Section: Introductionmentioning

confidence: 99%

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

Lutz

Coradi

Jaques

et al. 2022

J Food Process Engineering

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“…As consequence of biochemical processes, there is consumption of product energy reserves able to affect the germination potential and vigor in seeds (ZIEGLER et al, 2016;LISBOA et al, 2017;GOLDEN et al, 2018;SILVA et al, 2021). In the grain mass, infestations and hot spots cause an increase in the production of CO 2 , which moves in the intragranular spaces and can be detected by sensors (RAMACHANDRAN, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…The use of gas sensors in postharvest stages of agricultural products can help to maintain quality, to reduce potential damage and loss of commercial value (DELIBERADOR et al, 2019;CONAB, 2021;RAMACHANDRAN, 2022). In this way, the study aimed to develop and to analyze the efficiency of a low-cost system for monitoring CO 2 in stored grains and seeds.…”

Section: Introductionmentioning

confidence: 99%

CO2 monitoring system for storage of grains and seeds

Saath

Wenneck

Pereira

et al. 2022

Reveng

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The monitoring of grain and seeds characteristics is fundamental to maintain postharvest quality. Grain mass changes through CO2 levels monitoring can reduce the product deterioration by anticipating actions to maintain the grain mass quality. This study aimed to develop a CO2 monitoring system in stored grains/seeds and to analyze its efficiency. The analysis was performed in four crop species (brachiaria, sunflower, corn, and soybean) with different water content and sanity condition. The system hardware was composed of a controller micro board, CO2 sensor and data stored module. The system routine was developed in C++ language. The monitoring of CO2 and flammable gases was performed for system analysis and verification. The system monitoring developed showed efficiency in CO2 levels determination in stored grains/seeds and low-cost, being a viable and applicable tool aiming to the maintenance of postharvest quality.

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“…The quantity of loss during storage accounted for about 55 % of the total loss (Banga et al, 2018;Gitonga et al, 2013;Kumar and Kalita, 2017;Liu et al, 2017). In grain storage, grain mildew and insect pests are important reasons for grain quality decline, which not only cause a lot of economic losses but also have an impact on national food security (Feinstein, 1969;Abramson et al, 2005;Duan et al, 2019;Ramachandran, 2022).…”

Section: Related Workmentioning

confidence: 99%

Research on structural parameters and kinematic properties of a drill-in granary grain condition detector

Yin

Yu²,

Song³

et al. 2022

Mech. Sci.

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Abstract. In order to realize the real-time monitoring of the grain condition, grain condition information should be collected during storage operations. A drill-in granary grain condition detector which can drill into a grain pile in a granary is proposed. The kinematic models of four Archimedes screw mechanisms were established, and the motion characteristics of grain particles in spiral grooves were described. Several groups of single variable simulation experiments are designed to discuss the important factors affecting the motion performance of the detector. Based on a discrete element method with multi-body dynamics (DEM-MBD) simulation calculation, the force and velocity change images of grain particles were obtained to verify the feasibility of the scheme. The steering function of the detector is verified by simulation, and the relationship between the steering radius and the screw rotation speed difference is analyzed. The results show that the drill-in granary grain condition detector can move forward and turn when it is immersed in the grain pile. It has the characteristics of excellent flexibility and a high degree of freedom. It compensates for the current situation in which robots cannot go deep inside the grain pile and complete the steering.

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Integrated approach on stored grain quality management with CO2 monitoring-A review

Cited by 25 publications

References 93 publications

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

CO2 monitoring system for storage of grains and seeds

Research on structural parameters and kinematic properties of a drill-in granary grain condition detector

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